✍️ Free Response Questions (FRQs)
👆 Unit 1 - Exploring One-Variable Data
1.4Representing a Categorical Variable with Graphs
1.5Representing a Quantitative Variable with Graphs
1.6Describing the Distribution of a Quantitative Variable
1.7Summary Statistics for a Quantitative Variable
1.8Graphical Representations of Summary Statistics
1.9Comparing Distributions of a Quantitative Variable
✌️ Unit 2 - Exploring Two-Variable Data
2.0 Unit 2 Overview: Exploring Two-Variable Data
2.1Introducing Statistics: Are Variables Related?
2.2Representing Two Categorical Variables
2.3Statistics for Two Categorical Variables
2.4Representing the Relationship Between Two Quantitative Variables
2.8Least Squares Regression
🔎 Unit 3 - Collecting Data
3.5Introduction to Experimental Design
🎲 Unit 4 - Probability, Random Variables, and Probability Distributions
4.1Introducing Statistics: Random and Non-Random Patterns?
4.7Introduction to Random Variables and Probability Distributions
4.8Mean and Standard Deviation of Random Variables
4.9Combining Random Variables
4.11Parameters for a Binomial Distribution
📊 Unit 5 - Sampling Distributions
5.0Unit 5 Overview: Sampling Distributions
5.1Introducing Statistics: Why Is My Sample Not Like Yours?
5.4Biased and Unbiased Point Estimates
5.6Sampling Distributions for Differences in Sample Proportions
⚖️ Unit 6 - Inference for Categorical Data: Proportions
6.0Unit 6 Overview: Inference for Categorical Data: Proportions
6.1Introducing Statistics: Why Be Normal?
6.2Constructing a Confidence Interval for a Population Proportion
6.3Justifying a Claim Based on a Confidence Interval for a Population Proportion
6.4Setting Up a Test for a Population Proportion
6.6Concluding a Test for a Population Proportion
6.7Potential Errors When Performing Tests
6.8Confidence Intervals for the Difference of Two Proportions
6.9Justifying a Claim Based on a Confidence Interval for a Difference of Population Proportions
6.10Setting Up a Test for the Difference of Two Population Proportions
😼 Unit 7 - Inference for Qualitative Data: Means
7.1Introducing Statistics: Should I Worry About Error?
7.2Constructing a Confidence Interval for a Population Mean
7.3Justifying a Claim About a Population Mean Based on a Confidence Interval
7.4Setting Up a Test for a Population Mean
7.5Carrying Out a Test for a Population Mean
7.6Confidence Intervals for the Difference of Two Means
7.7Justifying a Claim About the Difference of Two Means Based on a Confidence Interval
7.8Setting Up a Test for the Difference of Two Population Means
7.9Carrying Out a Test for the Difference of Two Population Means
✳️ Unit 8 Inference for Categorical Data: Chi-Square
📈 Unit 9 - Inference for Quantitative Data: Slopes
🧐 Multiple Choice Questions (MCQs)
Best Quizlet Decks for AP Statistics
⏱️ 2 min read
June 11, 2020
In order to use binomial formulae for mean and standard deviation, you need to identify whether the conditions qualify for binomial distribution. If one of four conditions don’t work/occur, it is not a binomial setting. In other words, you can’t just use binomial distribution because you feel like it. 10% Condition: When taking a random sample of size n from a population of size N. we can use a binomial distribution to model the count of successes in the sample as long as n < 0.10N.
The four conditions for a binomial setting are Binary, Independent, Number, and Same Probability or BINS. Remember your BINS for the AP Stats exam questions.
Binary: The possible outcomes of each trial can be classified as a success or a failure.
Independent: Trials must be independent. That is, knowing the outcome of one trial must not tell us anything about the outcome of any other trial.
Number: The number of trials n of the chance process must be fixed in advance.
Same Probability: There is the same probability of success p on each trial.
Disclaimer: These formulae only work for binomial settings!
To find the mean of a binomial random variable: If a count X of successes has a binomial distribution with number of trials n and probability of success p, the mean (expected value) of X is mean = n*P.
To find the standard deviation of a binomial random variable: If a count X of successes has a binomial distribution with number of trials n and probability of success p, the standard deviation of X is standard deviation = np(1-p).
10% Condition: When taking a random sample of size n from a population of size N. we can use a binomial distribution to model the count of successes in the sample as long as n < 0.10N.
🎥Watch: AP Stats - Probability: Random Variables, Binomial/Geometric Distributions
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